Depth-dependent crossover for genetic programming

It is known that selection and crossover operators contribute to generating solutions in genetic programming (GP). Traditionally, crossover points are selected randomly by a normal (canonical) crossover. However, the traditional method has several difficulties, in that building blocks (i.e. effective partial programs) are broken because of blind application of the normal crossover. This paper proposes a depth-dependent crossover for GP, in which the depth selection ratio is varied according to the depth of a node. This proposed method accumulates building blocks via the encapsulation of the depth-dependent crossover. We compare the performance of GP with depth-dependent crossover with that with normal crossover. Our experimental results clarify that the superiority of the proposed crossover to the normal method